Human behaviour, NPI and mobility reduction effects on COVID-19 transmission in different countries of the world

Background The outbreak of Coronavirus disease, which originated in Wuhan, China in 2019, has affected the lives of billions of people globally. Throughout 2020, the reproduction number of COVID-19 was widely used by decision-makers to explain their strategies to control the pandemic. Methods In this work, we deduce and analyze both initial and effective reproduction numbers for 12 diverse world regions between February and December of 2020. We consider mobility reductions, mask wearing and compliance with masks, mask efficacy values alongside other non-pharmaceutical interventions (NPIs) in each region to get further insights in how each of the above factored into each region’s SARS-COV-2 transmission dynamic. Results We quantify in each region the following reductions in the observed effective reproduction numbers of the pandemic: i) reduction due to decrease in mobility (as captured in Google mobility reports); ii) reduction due to mask wearing and mask compliance; iii) reduction due to other NPI’s, over and above the ones identified in i) and ii). Conclusion In most cases mobility reduction coming from nationwide lockdown measures has helped stave off the initial wave in countries who took these types of measures. Beyond the first waves, mask mandates and compliance, together with social-distancing measures (which we refer to as other NPI’s) have allowed some control of subsequent disease spread. The methodology we propose here is novel and can be applied to other respiratory diseases such as influenza or RSV. Supplementary Information The online version contains supplementary material available at (10.1186/s12889-022-13921-3).


Next generation matrix and reduced Jacobian
The Jacobian matrix near the disease-free equilibrium (DFE, which consists of S(0) = N and I(0) = 0) for the system of equations (3) is: Using the next generation matrix method around the DFE, ( [1]) we compute R 0 as the largest eigenvalue of the matrix F V −1 . F and V are known as the transmission and the transition part, respectively. Consider a disease free population and an infected individual enter into a compartment, then each entry of matrix F represents the production of new infections between compartments, and each entry of matrix V −1 describes changes of this individual in each compartment. Then each entry of matrix F V −1 describes the expected number of infections that produced by the original entry infected. [2].
we obtain the largest eigenvalue of the matrix F V −1 , closed form expression: Further, using [3], we can compute the eigenvalues of the reduced Jacobian above and find that there is one positive eigenvalue (responsible for the growth near the DFE) which can be derived in closed form: which in turn can be solved for an expression of β as a function of the growth factor ρ near the DFE: Finally we can estimate R 0 as a function of the growth factor near the DFE in each region using (1) as: (2)

Mask Efficacy and Compliance Data
During the first several months of the pandemic there was considerable debate on the effect of face masks on limiting the spread of the COVID-19 pandemic. There was also debate on whether to recommend the general public to use a face mask. Later, articles, scientific reports, and data proved the impact of face masks in altering the outcomes of peak hospitalization [4]. There have been observational studies in health care workers which reported that wearing surgical masks and N95 masks can reduce the risks of respiratory illnesses by 40-60% [5].
Notably, face masks are found to be useful in both preventing asymptomatic transmission and illness in healthy persons. Moreover, varying efficacy and compliance of masks have an impact on the transmission dynamics and control of the COVID-19 pandemic [6]. A review [7] of observational studies estimates that surgical and comparable cloth masks are 67% effective in protecting the wearer. Some reports show that even a cotton T-shirt can block half of the inhaled aerosols and almost 80% of exhaled aerosols measuring 2µm across (e.g. unpublished work by Linsey Marr, an environmental engineer at Virginia Tech in Blacksburg). Furthermore, in [8] the mask wearing reduction factor is taken to be in a range of [30%, 80%], where 30% effectiveness is the level of a paper mask or 1-layer mask, while 80% and higher are surgical masks and N95 masks, which were not typically available to everyday individuals in 2020. We consider 50% to be the mask efficacy in our model .
The proportion of a population wearing face masks differs across countries/regions based on social norms, political reasons, the consequences of non-compliance e.g., fines. The results from a study surveying compliance are for example: • The Institute for Health Metrics and Evaluation (IHME), a global health research center at the University of Washington [9] is reported the percentage of mask use in Italy was between 63% to 93% from September 1st till December 31, 2020, Sweden 1-7% , Saudi Arabia 73-76%, Ontario 75-85%, Florida 66-70%, Romania 63-86%, Ghana 50-36%, South Africa 80-81%, Indonesia 74-76%, Nepal 64-63%, Brazil 68-59%, Argentina 89-83%. • According to data from the Institute for Health Metrics and Evaluation at the University of Washington in Seattle, mask use has held steady around 50% since late July in the United States. It was predicted to increase to 95% as of 23 September. (see [9]) Whereas, a survey from Gallup [10] shows 72% of U.S. adults say they either always wear a face mask or wear one often when going to public places. • Percentage of people who worn a face mask outside their home always is reported 93.9% in Italy and 12.1% in Sweden 12.1% by YouGov; Imperial College London [11]. We adapt our SEIRL model with the compliance of mask-wearing value denoted as compliance m obtained from [9]. Table 1 represents the mask use for each region under study.